There are many conventional traffic detection systems. Conventional systems typically utilize sensors, either in the roadway itself, or positioned at a roadside location or on traffic lights proximate to the roadway. The most common type of vehicular sensors are inductive coils, or loops, embedded in a road surface. Other existing systems utilize video cameras, radar sensors, acoustic sensors, or magnetometers, either in the road itself, or at either the side of a roadway or positioned higher above traffic to observe and detect vehicles in a desired area. Each of these sensors provide information used to determine a presence of vehicles in specific lanes in intersections, to provide information to traffic signals for proper actuation.
These conventional detection systems are commonly set up with ‘virtual zones’, which are hand- or machine-drawn areas on an image where objects may be moving or present. Traditionally, a vehicle passes through or stops in a zone, and these zones generate an “output” when an object is detected as passing through or resting within all or part of the zone.
Many detection systems are capable of detecting different types of vehicles, such as cars, trucks, bicycles, motorcycles, pedestrians, etc. This is accomplished by creating special zones within a field of view to differentiate objects, such as bicycle zones and pedestrian zones. Therefore, conventional detection systems are capable of differentiating, for example, bicycles from other types of vehicles by analyzing these special zones. However, one limitation of this approach is that multiple zones have to be drawn, often over the top of each other at the same location, to be able to provide outputs for different modes. Therefore there is a need in the art for a system and method which is capable of differentiating between objects in only one zone within an area of traffic detection.
Outputs are sent to a traffic signal controller, which performs control and timing functions based on the information provided. These outputs also provide traffic planners and engineers with information on the volume of traffic at key points in a traffic network. This information is important for comparing volumes over periods of time to help with accurate adjustment of signal timing and managing traffic flow. Current systems and methods of traffic detection provide data that results only from a count of a total number of vehicles, which may or may not include bicycles or other road users, as therefore there is no way differentiating between different types of vehicles. As the need for modified signal timing to accommodate bicyclists, pedestrians and others becomes more critical for proper traffic management, a method for separating the count of all modes of use on a thoroughfare is needed to improve the ability to accurately manage traffic environments.